{
  "abstract": "Automatic sleep staging is a critical task in healthcare due to the global prevalence of sleep disorders. This study focuses on single-channel electroencephalography (EEG), a practical and widely available signal for automatic sleep staging. Existing approaches face challenges such as class imbalance, limited receptive-field modeling, and insufficient interpretability. This work proposes TempoSleep, a context-aware framework for single-channel EEG sleep staging, with particular emphasis on improving detection of the N1 stage. Many prior models operate as black boxes with stacked layers, lacking clearly defined and interpretable feature extraction roles. TempoSleep combines compact multi-scale feature extraction with temporal modeling to capture both local and long-range dependencies. To address data imbalance, especially in the N1 stage, class-weighted loss functions and data augmentation are applied. EEG signals are segmented into sub-epoch chunks, and final predictions are obtained by averaging softmax probabilities across chunks, enhancing contextual representation and robustness. The proposed framework achieves an overall accuracy of 89.72% and a macro-average F1-score of 85.46%. Notably, it attains an F1-score of 61.7% for the challenging N1 stage, demonstrating a substantial improvement over previous methods on the SleepEDF datasets.These results indicate that the proposed approach effectively improves sleep staging performance while providing insights into its prediction behavior and supporting its potential application in automatic sleep staging.",
  "abstract_shown": true,
  "affiliations": [
    "Faculty of Computer Science and Engineering, Shahid Beheshti University, Tehran, Iran"
  ],
  "article_type": "Research Article",
  "arxiv": null,
  "author_total": 3,
  "authors": [
    {
      "affs": [
        0
      ],
      "name": "Amir Ali Vakili"
    },
    {
      "name": "Salar Jahanshiri"
    },
    {
      "name": "Armin Salimi-Badr"
    }
  ],
  "bibtex": "https://research.draconitic.com/paper/0sd6vq2t97sbj.bib",
  "cited_by": 0,
  "doi": "10.1371/journal.pone.0358241",
  "fulltext_url": null,
  "id": "0sd6vq2t97sbj",
  "issns": [
    "1932-6203"
  ],
  "landing_url": "https://doi.org/10.1371/journal.pone.0358241",
  "language": null,
  "licence": "CC-BY-4.0",
  "licence_label": "CC BY 4.0",
  "licence_source": "journal",
  "licence_url": "https://creativecommons.org/licenses/by/4.0/",
  "metadata_licence": null,
  "native_subjects": [
    [
      "openalex",
      "2805|EEG and Brain-Computer Interfaces"
    ],
    [
      "plos",
      "/Biology and life sciences/Neuroscience/Brain mapping/Electroencephalography"
    ],
    [
      "plos",
      "/Biology and life sciences/Neuroscience/Neural networks/Recurrent neural networks"
    ],
    [
      "plos",
      "/Biology and life sciences/Neuroscience/Neuroimaging/Electroencephalography"
    ],
    [
      "plos",
      "/Biology and life sciences/Neuroscience/Neurophysiology/Brain electrophysiology/Electroencephalography"
    ],
    [
      "plos",
      "/Biology and life sciences/Physiology/Electrophysiology/Neurophysiology/Brain electrophysiology/Electroencephalography"
    ],
    [
      "plos",
      "/Biology and life sciences/Physiology/Physiological processes/Sleep"
    ],
    [
      "plos",
      "/Computer and information sciences/Artificial intelligence/Machine learning"
    ],
    [
      "plos",
      "/Computer and information sciences/Artificial intelligence/Machine learning/Deep learning"
    ],
    [
      "plos",
      "/Computer and information sciences/Data management/Data compression"
    ],
    [
      "plos",
      "/Computer and information sciences/Neural networks/Recurrent neural networks"
    ],
    [
      "plos",
      "/Computer and information sciences/Software engineering/Preprocessing"
    ],
    [
      "plos",
      "/Engineering and technology/Software engineering/Preprocessing"
    ],
    [
      "plos",
      "/Medicine and health sciences/Clinical medicine/Clinical neurophysiology/Electroencephalography"
    ],
    [
      "plos",
      "/Research and analysis methods/Bioassays and physiological analysis/Electrophysiological techniques/Brain electrophysiology/Electroencephalography"
    ],
    [
      "plos",
      "/Research and analysis methods/Imaging techniques/Neuroimaging/Electroencephalography"
    ],
    [
      "plos",
      "/Research and analysis methods/Mathematical and statistical techniques/Mathematical functions/Convolution"
    ]
  ],
  "openalex": "W7117773445",
  "pdf_url": "https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0358241&type=printable",
  "pmcid": null,
  "pmid": null,
  "published": "2026-09-30",
  "publisher": "Public Library of Science",
  "ris": "https://research.draconitic.com/paper/0sd6vq2t97sbj.ris",
  "sources": [
    {
      "first_seen": "2026-10-30T12:17:37Z",
      "native_id": "10.1371/journal.pone.0358241",
      "source": "plos",
      "url": "https://doi.org/10.1371/journal.pone.0358241"
    }
  ],
  "subjects": [
    {
      "field": "biology",
      "origin": "plos:neuroscience",
      "subfield": "neuroscience"
    },
    {
      "field": "medicine",
      "origin": "plos:physiology",
      "subfield": "physiology"
    }
  ],
  "title": "A context-aware temporal modeling through unified multi-scale temporal encoding and hierarchical sequence learning for single-channel EEG sleep staging",
  "title_text": "A context-aware temporal modeling through unified multi-scale temporal encoding and hierarchical sequence learning for single-channel EEG sleep staging",
  "type": "peer-reviewed",
  "updated_at": "2026-10-30T12:17:37Z",
  "url": "https://research.draconitic.com/paper/0sd6vq2t97sbj",
  "venue": "PLOS One",
  "versions": []
}